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Expo Brownfield

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expo
expo-brownfield

Framework (OSS). Integrate Expo and React Native into an existing native iOS or Android app. Use for brownfield, embedding a React Native screen in SwiftUI/UIKit or Kotlin, or AAR/XCFramework packaging. Covers isolated and integrated approaches. For building or distributing a purely native app with EAS, use eas-app-stores.

Overview

Publisherexpo
Repositoryskills
Skill nameexpo-brownfield
Stars
2.5K
Forks
146
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by expo on GitHub. Read the source before you install it.

Installation

Install the Expo Brownfield AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/expo/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/expo/skills/expo-brownfield .claude/skills/expo-brownfield
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Expo Brownfield in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Expo Brownfield on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Expo Brownfield is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Expo Brownfield

A brownfield app is an existing native iOS or Android app that adopts React Native incrementally, as opposed to a greenfield app that is React Native from day one.

Inspect the host first

Identify the existing app entry point, navigation owner, native build system, deployment targets, and any React Native runtime already linked. Record the installed Expo, React Native, and brownfield package versions from the lockfile. Adding EAS Build or Submit to a Swift app alone does not require React Native; route that task to eas-app-stores.

Preserve the host's SwiftUI App / UIKit window and native screens when embedding a feature. Do not run prebuild in a manually maintained native host, including during troubleshooting. An isolated Expo producer may use CNG; keep its generated ios/ and android/ separate from the consuming app.

Expo supports two distinct ways to add React Native to a brownfield project:

ApproachWhat ships to the native appWhen to choose
IsolatedPrebuilt AAR / XCFrameworkNative team doesn't need Node or RN tooling; RN code can live in a separate repo
IntegratedReact Native sources added to the existing Gradle / CocoaPods buildOne team owns everything; comfortable with RN tooling; wants a single build

For the full decision matrix, see ./references/comparison.md.

Pick an approach

Use these quick rules — fall through to comparison.md for anything ambiguous.

  • Choose isolated if the iOS/Android team must consume RN as a regular library dependency (AAR or XCFramework), without installing Node, Yarn, or the React Native build toolchain.
  • Choose isolated if RN code and native code live in separate repositories or release on independent cadences.
  • Choose integrated if a single team owns both the native and RN code and is willing to add React Native + Expo to the native project's Gradle and CocoaPods setup.
  • Both approaches support Metro and Fast Refresh in Debug. Choose integrated for shared build ownership, not because isolated lacks live JS iteration.

References

  • ./references/brownfield-isolated.md -- Build RN as AAR/XCFramework and consume from the native app (BrownfieldActivity, ReactNativeViewController, ReactNativeView)
  • ./references/brownfield-integrated.md -- Add RN and Expo directly to existing Gradle and CocoaPods builds, preserving the native app shell
  • ./references/feature-integration.md -- Pass input, return results, dismiss, clean up listeners, and forward lifecycle events; includes a SwiftUI host example
  • ./references/comparison.md -- Decision criteria, trade-offs, and scenario mapping for choosing an approach
  • ./references/troubleshooting.md -- Metro connection, build, signing, and module-resolution issues common to both approaches

More information available at https://docs.expo.dev/brownfield/overview/

Shared prerequisites

Both approaches require, in the environment that builds the React Native side:

  • Node.js (LTS) — runs the Expo CLI and JavaScript code.
  • The project's package manager and lockfile — npm, Yarn, pnpm, or Bun. Do not switch package managers just to follow an example.

The iOS build environment needs Xcode and CocoaPods (use the project's Gemfile/Bundler setup when present). The isolated consuming app needs Xcode but no CocoaPods or RN tooling just to consume the artifacts.

Select compatible versions

For an existing Expo/RN project, keep its selected SDK and use npx expo install to align dependencies. Do not upgrade it just to follow this skill. For a new producer, use the current stable SDK compatible with the host's OS support, dependencies, and build toolchain; confirm the release is stable before selecting it.

Before native setup, read ./references/version-compatibility.md for matching native templates, toolchain/OS requirements, and build defaults across SDK versions. A purely native consumer has no Expo SDK version to pin, but must satisfy the artifact's requirements.

Verify the feature in the host

Open the RN screen with input, return a result to native, dismiss, and reopen with fresh input. Check listener cleanup and the host's original navigation. Then build the host in Release with a Release artifact and Metro stopped. Rendering only in Expo Go or the producer's example app does not validate the integration. See ./references/feature-integration.md for the complete acceptance scenario.

Submitting Feedback

If you encounter errors, misleading or outdated information in this skill, report it so Expo can improve:

bash
npx --yes submit-expo-feedback@latest --category skills --subject "expo-brownfield" "<actionable feedback>"

Only submit when you have something specific and actionable to report. Include as much relevant context as possible. If an AI agent repeatedly failed or the user had to take over an Expo task, load the expo-skill-feedback skill and follow its eval-candidate flow instead of reusing the command above.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Expo Brownfield AI skill do?

Framework (OSS). Integrate Expo and React Native into an existing native iOS or Android app. Use for brownfield, embedding a React Native screen in SwiftUI/UIKit or Kotlin, or AAR/XCFramework packaging. Covers isolated and integrated approaches. For building or distributing a purely native app with EAS, use eas-app-stores.

Why use Expo Brownfield on TypingMind?

Because you install it once and use it with any model. Expo Brownfield is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Expo Brownfield in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/expo/skills/tree/main/plugins/expo/skills/expo-brownfield. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Expo Brownfield?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Expo Brownfield?

As many as you like. As long as a model supports skills, you can use Expo Brownfield with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Expo Brownfield AI skill free?

Yes. It is published on GitHub by expo under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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